1. Field of the Invention
The present invention relates to a system for assisting decision-making utilizing an apparatus, such as a computer or a game machine for home use, that is capable of carrying out high-speed calculation and has a storage device for storing data (hereinafter simply referred to as a calculating unit), and more particularly, to a system for assisting consideration of selection that is capable of taking into account items, to which a considerer who is considering selecting at least one candidate out of selection candidates in at least one field attaches significance in the field, in evaluating the selection candidates to present a selection candidate that should be most recommended to the considerer.
2. Description of the Related Art
As an example of a method of assisting decision-making, commodity (i.e., consumer goods and services) recommendations to consumers by printed media, such as an information magazine (for example, Consumer Reports, etc.), have been conventionally well known. For example, as it is found in an automobile magazine and a personal computer magazine, there is a type of printed media, in which recommended merchandizes that are ranked relatively higher according to evaluation criteria unique to the magazine, are extensively introduced. Otherwise, there is another type of printed media, in which readers are categorized according to several conditions and are introduced an optimal recommended merchandizes for each category.
However, on the premise that the printed media are used by general individuals, the printed media have physical limitation in showing information and can include about 1,000 pages at the most. Therefore, there are the following disadvantages.    (1) The number of merchandizes that can be introduced is limited and recommendation is possible only for new merchandizes or merchandizes that are much talked about.    (2) Limitation arises in categorization of consumers and consumers can be categorized only by perfunctory classes such as an age, an occupation and a purpose of purchase.As a result, it is theoretically impossible in the first place to recommend an optimal commodity for each consumer.
In order to overcome such disadvantages, a system for recommending commodity using a computer has been proposed as described in Japanese Patent Laid-Open No. JP.97-204445A. According to the method described therein, an amount of information able to be treated is remarkably increased and a wide variety of personal data including history of purchases, characteristics and preferences of the consumer can be prepared. Thus, it becomes possible to perform commodity recommendation that matches more detailed categorization of considers.
However, since this method matches conditions or the like that a considerer wishes the commodity to have, with characteristics of each commodity in order to narrow down recommended commodity, the method is the same as a search engine on the Internet in that it retrieves information or documents including the information or sites where the information exists by way of narrowing down corresponding to presented conditions. Even if the conditions are defined precisely, a degree of significance given to each condition varies for each considerer. This point is not taken into account at all, because all the conditions are treated equally.
A search engine is a tool for retrieving information required by a considerer from a large amount of information on the Internet. However, the present situation is that “even if we try to narrow information by keywords, the narrowing down cannot be attained because information is collected too much” (Japan Economic Journal, Aug. 26, 2000, Morning edition (Plus 1) page 14S) and a limit to simple non-organic retrieval has begun to appear.
Moreover, currently, it is possible to retrieve and obtain information from a personal computer of a person who permits to provide the information, by software called Gnutella (see “Sentaku” August 2000, pages 99 to 99). In fact, roles that have been played by the search engine are the same as this Gnutella. The search engine simply shows where digital information satisfying a given condition exists but never determines how useful the information is for each considerer. It is obvious that this drawback also applies to the system described in Japanese Patent laid-Open No. JP.97-204445A that simply narrow information according to presented conditions.
In addition, recently there exists a web page (http://smartwoman.nikkei.co.jp/) for preparing an asset management portfolio by asking a user to provide information on an amount of their assets and a management attitude of the user.
However, even on this web page, a combination of optimal asset management methods is merely presented according to a result of answers to questions and it is not reflected on the result of answers how much significance a user attaches and to which item the user attaches significance, similarly to the method of the narrowing retrieval as described above.
As described above, the conventional recommendation method essentially has a limitation in that it cannot reflect a degree of significance that a searcher gives to desired conditions on a recommendation as in the method using the search engine. As an example specifically showing the limitation, a case in which an overseas travel is recommended will be described hereinbelow.
It is assumed as data that the sea (for example, ocean) is fairly clean, peace and order is fairly good and prices are fairly low in the Caribbean, the sea is very clean and peace and order is fairly good but prices are a little high in Greece and the sea is very clean but peace and order is bad and prices are very high in Indonesia.
When a consumer wishes to travel overseas to a destination where “the sea is clean, peace and order is good and prices are low”, since destinations are categorized based on whether or not a destination matches conditions in the conventional method, only the Caribbean, which satisfies the above-mentioned three conditions, is recommended, whereas other destinations are not recommended. However, although this recommendation is appropriate if the consumer cares about the above-mentioned three conditions equally, it is quite doubtful that the recommendation is correct or not if the consumer does not care about the conditions equally.
For example, if the consumer attaches the most significance to the condition “the sea is clean” among the above-mentioned three conditions, it is likely that the recommendation of Greece is more appropriate than that of the Caribbean. However, the destinations other than the Caribbean are never recommended with the conventional method of picking up and recommending a commodity that matches the conditions (i.e., excluding commodity that does not match the conditions).
Namely, with the conventional recommendation method, a result of calculating a commodity evaluation by taking into account a degree of significance for a consumer given to each evaluation item is not presented. Thus, an option that better matches the consumer's needs may not be recommended.
Conventionally, if a consumer is not satisfied with a recommendation result in this way, the consumer must answer questions again in order to obtain another recommendation result. However, in this case, the consumer must estimate by oneself how the consumer should change answers to the questions in order to derive another recommendation result. In addition, even a retrieval result obtained by answering the questions again in this way still does not reflect a degree-of-significance for the consumer given to each answer item.
Moreover, with the conventional recommendation method, evaluation items such as “cleanness of the sea”, “peace and order”, “prices” and “convenience of transportation” are treated equally as unrelated to and independent from each other. However, for example, the items “peace and order” and “convenience of transportation” are not always unrelated. It is possible that an interrelation exists between both the items in that transportation becomes inconvenient if peace and order is bad and transportation becomes convenient it peace and order is good. In this way, since there are interrelations of various strengths among the evaluation items, it is difficult to conclude that all the evaluation items are completely independent. Therefore, if the evaluation items are evaluated as independent from and unrelated to each other, there arises a limitation in the accuracy of recommendation.
As described above, in hopes that a consumer vaguely cherishes a commodity a plurality of evaluation items may be inextricably linked and a recommendation may be inappropriate simply by summing evaluations. Conversely, if answers to a plurality of questions are obtained from a consumer, a true degree of significance held by the consumer with respect to a certain single evaluation item may be derived.